


'''simple docstring'''




def   _a		(lowercase__			:   str							,   lowercase__			:   str						)			->			Dict:






     """simple docstring"""
     assert x is not None
     assert y is not None

     __snake_case           =						len(lowercase__						)
     __snake_case           =						len(lowercase__						)

     # declaring the array for storing the dp values
     __snake_case           =						[[0] * (n + 1) for _ in range(m + 1						)]  # noqa: E741

     for i in range(1							,   m + 1						):
          for j in range(1							,   n + 1						):
               __snake_case           =						1 if x[i - 1] == y[j - 1] else 0

               __snake_case           =						max(l[i - 1][j]							,   l[i][j - 1]							,   l[i - 1][j - 1] + match						)

     __snake_case           =						''
     __snake_case      ,       __snake_case           =						m, n
     while i > 0 and j > 0:
          __snake_case           =						1 if x[i - 1] == y[j - 1] else 0

          if l[i][j] == l[i - 1][j - 1] + match:
               if match == 1:
                    __snake_case           =						x[i - 1] + seq
               i -= 1
               j -= 1
          elif l[i][j] == l[i - 1][j]:
               i -= 1
          else:
               j -= 1

     return l[m][n], seq


if __name__ == "__main__":
       _a   :   Dict												=   "AGGTAB"
       _a   :   Any												=   "GXTXAYB"
       _a   :   Dict												=   4
       _a   :   Optional[int]												=   "GTAB"

       _a					,     _a   :   Union[str, Any]												=   longest_common_subsequence(a, b)
       print("len =", ln, ", sub-sequence =", subseq)
       import doctest

       doctest.testmod()












"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case : Any     		=		logging.getLogger(__name__)
snake_case : Optional[Any]     		=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case : Optional[Any]     		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a_)}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}							,	)
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                  raise ValueError(
                      '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """The input training data file (a text file)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
    __UpperCAmelCase           = field(
        default=5							,	metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The number of processes to use for the preprocessing."""}							,	)
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }							,	)







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.train_file is not None:
                  __magic_name__      				=    self.train_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
           if self.validation_file is not None:
                  __magic_name__      				=    self.validation_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							A						(				__snake_case: Tuple ,     __snake_case: str  )    ->		Tuple:

       """simple docstring"""





       with open(__snake_case ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.loads(__snake_case  ) for line in f.read().splitlines() if (len(__snake_case  ) > 0 and not line.isspace())]
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    {c: dataset[c] for c in dataset.column_names}
       __magic_name__      				=    refs
       return Dataset.from_dict(__snake_case  )


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank  ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s' ,     __snake_case  )

       # Set seed before initializing model.
       set_seed(training_args.seed  )

       # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
       # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
       # (the dataset will be downloaded automatically from the datasets Hub).
       #
       # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
       # 'text' is found. You can easily tweak this behavior (see below).
       #
       # In distributed training, the load_dataset function guarantee that only one local process can concurrently
       # download the dataset.
       if data_args.dataset_name is not None:
              # Downloading and loading a dataset from the hub.
              __magic_name__      				=    load_dataset(data_args.dataset_name ,     data_args.dataset_config_name  )
              if "validation" not in datasets.keys():
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[:{data_args.validation_split_percentage}%]""" ,     )
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[{data_args.validation_split_percentage}%:]""" ,     )
       else:
              __magic_name__      				=    {}
              if data_args.train_file is not None:
                     __magic_name__      				=    data_args.train_file
              if data_args.validation_file is not None:
                     __magic_name__      				=    data_args.validation_file
              __magic_name__      				=    data_args.train_file.split('.'  )[-1]
              if extension == "txt":
                     __magic_name__      				=    'text'
              __magic_name__      				=    load_dataset(__snake_case ,     data_files=__snake_case  )
       # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
       # https://huggingface.co/docs/datasets/loading_datasets.html.

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.config_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    CONFIG_MAPPING[model_args.model_type]()
              logger.warning('You are instantiating a new config instance from scratch.'  )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""  )
                     config.update_from_string(model_args.config_overrides  )
                     logger.info(F"""New config: {config}"""  )

       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'use_fast': model_args.use_fast_tokenizer,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.tokenizer_name:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.tokenizer_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              raise ValueError(
                  'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
                  'You can do it from another script, save it, and load it from here, using --tokenizer_name.'  )

       if model_args.model_name_or_path:
              __magic_name__      				=    AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              logger.info('Training new model from scratch'  )
              __magic_name__      				=    AutoModelForMaskedLM.from_config(__snake_case  )

       model.resize_token_embeddings(len(__snake_case  )  )

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              __magic_name__      				=    datasets['train'].column_names
       else:
              __magic_name__      				=    datasets['validation'].column_names
       __magic_name__      				=    'text' if 'text' in column_names else column_names[0]

       __magic_name__      				=    'max_length' if data_args.pad_to_max_length else False

       def tokenize_function(__snake_case: str  ):
              # Remove empty lines
              __magic_name__      				=    [line for line in examples['text'] if len(__snake_case  ) > 0 and not line.isspace()]
              return tokenizer(examples['text'] ,     padding=__snake_case ,     truncation=__snake_case ,     max_length=data_args.max_seq_length  )

       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     num_proc=data_args.preprocessing_num_workers ,     remove_columns=[text_column_name] ,     load_from_cache_file=not data_args.overwrite_cache ,     )

       # Add the chinese references if provided
       if data_args.train_ref_file is not None:
              __magic_name__      				=    add_chinese_references(tokenized_datasets['train'] ,     data_args.train_ref_file  )
       if data_args.validation_ref_file is not None:
              __magic_name__      				=    add_chinese_references(
                  tokenized_datasets['validation'] ,     data_args.validation_ref_file  )
       # If we have ref files, need to avoid it removed by trainer
       __magic_name__      				=    data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              __magic_name__      				=    False

       # Data collator
       # This one will take care of randomly masking the tokens.
       __magic_name__      				=    DataCollatorForWholeWordMask(tokenizer=__snake_case ,     mlm_probability=data_args.mlm_probability  )

       # Initialize our Trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None ,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
                     __magic_name__      				=    model_args.model_name_or_path
              else:
                     __magic_name__      				=    None
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'train_results.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Train results *****'  )
                            for key, value in sorted(train_result.metrics.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir ,     'trainer_state.json'  )  )

    # Evaluation
       __magic_name__      				=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'  )

              __magic_name__      				=    trainer.evaluate()

              __magic_name__      				=    math.exp(eval_output['eval_loss']  )
              __magic_name__      				=    perplexity

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'eval_results_mlm_wwm.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Eval results *****'  )
                            for key, value in sorted(results.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

       return results


def 							A						(				__snake_case: List[str]  )    ->		Union[str, Any]:

       """simple docstring"""





       main()


if __name__ == "__main__":
 main()